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Record W2059482672 · doi:10.1310/tsr1405-1

Functional Mobility Training for Individuals Admitted to Acute Care Following a Stroke: A Prospective Study

2007· article· en· W2059482672 on OpenAlexafffundabout
Lisa M. Masters, Susan Barreca, Barb Ansley, Kelly Waid, Shannon Buckley

Bibliographic record

VenueTopics in Stroke Rehabilitation · 2007
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsSt. Peter's HospitalHamilton Regional Laboratory Medicine ProgramHamilton Health Sciences
FundersOntario Ministry of Health and Long-Term CareHamilton Health Sciences
KeywordsAcute strokeStroke (engine)MedicineAcute careHealth carePhysical therapyEmergency medicineMedical emergencyNursingEmergency department

Abstract

fetched live from OpenAlex

PURPOSE: This study describes current stroke care within hospital acute care settings. METHOD: Twenty-two acute care hospital sites in Central South Ontario were mailed a survey exploring the prevalence of stroke admissions, use of protocols and policies, staff resources, stroke-specific training, and available equipment. Corresponding site data from the Canadian Institute for Health Information were also analyzed. RESULTS: An 82% survey response rate was obtained. In 2003-2004, stroke admissions represented 1.9% of total admissions, with a mean admitting resource intensity weight of 1.99. Average length of stay was 12.5 days, with 3.4 of these days designated awaiting an alternate level of care. One third of the sites reported that they had no written guidelines on how to position or mobilize individuals following a stroke, and very few of the sites reported providing stroke-specific education. CONCLUSION: The lack of a consistent coordinated approach to early mobilization and physical care for individuals admitted to an acute care setting following a stroke necessitates that new opportunities to coordinate educational resources and services to promote evidence-based practice in acute stroke care be pursued.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.027
GPT teacher head0.345
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2007
Admission routes3
Has abstractyes

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